Results 21 to 30 of about 14,098 (299)
An efficient sparse grid Galerkin approach for the numerical valuation of basket options under Kou's jump-diffusion model [PDF]
, 2022 S.121-150We use a sparse grid approach to discretize a multi-dimensional partial integro-differential equation (PIDE) for the deterministic valuation of European put options on Kou's jump-diffusion processes.
Hullmann, A., Griebel, M.
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sparse-ir: Optimal compression and sparse sampling of many-body propagators
SoftwareX, 2023 We introduce sparse-ir, a collection of libraries to efficiently handle imaginary-time propagators, a central object in finite-temperature quantum many-body calculations. We leverage two concepts: firstly, the intermediate representation (IR), an optimal
Markus Wallerberger +17 more
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On the convergence of the combination technique [PDF]
, 2013 Sparse tensor product spaces provide an efficient tool todiscretize higher dimensional operator equations. The direct Galerkin method in such ansatz spaces may employ hierarchical bases, interpolets, wavelets or multilevel frames. Besides, an alternative
Griebel, Michael +3 more
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Sensors, 2019 Exploring large, unknown, and unstructured environments is challenging for Unmanned Aerial Vehicles (UAVs), but they are valuable tools to inspect large structures safely and efficiently.
Margarida Faria +4 more
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Entropy, 2013 Molecular modeling is an important subdomain in the field of computational modeling, regarding both scientific and industrial applications. This is because computer simulations on a molecular level are a virtuous instrument to study the impact of ...
Dirk Reith, Marco Hülsmann
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Uniform hypergraphs containing no grids [PDF]
, 2013 A hypergraph is called an r×r grid if it is isomorphic to a pattern of r horizontal and r vertical lines, i.e.,a family of sets {A1, ..., Ar, B1, ..., Br} such that Ai∩Aj=Bi∩Bj=φ for 1 ...
Füredi, Zoltán, Ruszinkó, Miklós
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Sparse grid distance transforms
Graphical Models, 2010 We present a Sparse Grid Distance Transform (SGDT), an algorithm for computing and storing large distance fields. Although SGDT is based on a divide-and-conquer algorithm for distance transforms, its data structure is quite simplified. Our observations revealed that distance fields can be recovered from distance fields of sub-block cluster boundaries ...
Takashi Michikawa, Hiromasa Suzuki
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Heterogeneous Distributed Big Data Clustering on Sparse Grids
Algorithms, 2019 Clustering is an important task in data mining that has become more challenging due to the ever-increasing size of available datasets. To cope with these big data scenarios, a high-performance clustering approach is required.
David Pfander +2 more
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Shear turbulence on a sparse spectral grid [PDF]
Physical Review E, 2007 We simulate turbulence in a plane Couette geometry by a spectral method intermediate between full resolution and the complete elimination of small modes common in large eddy simulations. The wave number grid is sparse in spanwise and downstream direction, with a total number of modes proportional to Re(3/4) lnRe.
F, De Lillo, Bruno, Eckhardt
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Adaptive Sparse Grid Classification Using Grid Environments [PDF]
, 2007 Common techniques tackling the task of classification in data mining employ ansatz functions associated to training data points to fit the data as well as possible. Instead, the feature space can be discretized and ansatz functions centered on grid points can be used.
Dirk Pflüger +2 more
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